35 research outputs found
InversionNet3D: Efficient and Scalable Learning for 3D Full Waveform Inversion
Seismic full-waveform inversion (FWI) techniques aim to find a
high-resolution subsurface geophysical model provided with waveform data. Some
recent effort in data-driven FWI has shown some encouraging results in
obtaining 2D velocity maps. However, due to high computational complexity and
large memory consumption, the reconstruction of 3D high-resolution velocity
maps via deep networks is still a great challenge. In this paper, we present
InversionNet3D, an efficient and scalable encoder-decoder network for 3D FWI.
The proposed method employs group convolution in the encoder to establish an
effective hierarchy for learning information from multiple sources while
cutting down unnecessary parameters and operations at the same time. The
introduction of invertible layers further reduces the memory consumption of
intermediate features during training and thus enables the development of
deeper networks with more layers and higher capacity as required by different
application scenarios. Experiments on the 3D Kimberlina dataset demonstrate
that InversionNet3D achieves state-of-the-art reconstruction performance with
lower computational cost and lower memory footprint compared to the baseline
Edge-InversionNet: Enabling Efficient Inference of InversionNet on Edge Devices
Seismic full waveform inversion (FWI) is a widely used technique in
geophysics for inferring subsurface structures from seismic data. And
InversionNet is one of the most successful data-driven machine learning models
that is applied to seismic FWI. However, the high computing costs to run
InversionNet have made it challenging to be efficiently deployed on edge
devices that are usually resource-constrained. Therefore, we propose to employ
the structured pruning algorithm to get a lightweight version of InversionNet,
which can make an efficient inference on edge devices. And we also made a
prototype with Raspberry Pi to run the lightweight InversionNet. Experimental
results show that the pruned InversionNet can achieve up to 98.2 % reduction in
computing resources with moderate model performance degradation
Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness
Full waveform inversion (FWI) infers the subsurface structure information
from seismic waveform data by solving a non-convex optimization problem.
Data-driven FWI has been increasingly studied with various neural network
architectures to improve accuracy and computational efficiency. Nevertheless,
the applicability of pre-trained neural networks is severely restricted by
potential discrepancies between the source function used in the field survey
and the one utilized during training. Here, we develop a Fourier-enhanced deep
operator network (Fourier-DeepONet) for FWI with the generalization of seismic
sources, including the frequencies and locations of sources. Specifically, we
employ the Fourier neural operator as the decoder of DeepONet, and we utilize
source parameters as one input of Fourier-DeepONet, facilitating the resolution
of FWI with variable sources. To test Fourier-DeepONet, we develop two new and
realistic FWI benchmark datasets (FWI-F and FWI-L) with varying source
frequencies and locations. Our experiments demonstrate that compared with
existing data-driven FWI methods, Fourier-DeepONet obtains more accurate
predictions of subsurface structures in a wide range of source parameters.
Moreover, the proposed Fourier-DeepONet exhibits superior robustness when
dealing with noisy inputs or inputs with missing traces, paving the way for
more reliable and accurate subsurface imaging across diverse real conditions